Machine Learning for Anastomotic Leak Prediction in Colorectal Surgery—Between Validation and Clinical Implementation

Despite decades of research and the proposal of numerous risk scores and prediction models, reliably identifying patients at risk for anastomotic leakage (AL) remains challenging-even for experienced surgeons.1,2 AL contributes substantially to postoperative morbidity, mortality, and prolonged hospitalization, yet its occurrence often defies conventional clinical expectations.3 It may occur in patients without obvious risk factors, whereas others with multiple comorbidities recover uneventfully.This unpredictability highlights the limitations of existing approaches and has prompted increasing interest in artificial intelligence-driven risk prediction models.Machine learning algorithms, in particular, offer the capacity to identify complex, nonlinear associations among clinical variables that may enable more accurate and individualized estimation of AL risk than traditional scoring systems or clinical judgment alone.4,5 Nevertheless, accurate preoperative risk stratification for AL in individual patients remains limited, underscoring the ongoing challenge of translating population-level predictors into clinically actionable decisions at the bedside.